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---
license: apache-2.0
tags:
- image-segmentation
- vision
- generated_from_trainer
model-index:
- name: segformer-finetuned-Maize-10k-steps-sem
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# segformer-finetuned-Maize-10k-steps-sem
This model is a fine-tuned version of [nvidia/mit-b5](https://huggingface.co/nvidia/mit-b5) on the koushikn/Maize_sem_seg dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0756
- Mean Iou: 0.9172
- Mean Accuracy: 0.9711
- Overall Accuracy: 0.9804
- Accuracy Background: 0.9834
- Accuracy Maize: 0.9588
- Iou Background: 0.9779
- Iou Maize: 0.8566
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.001
- train_batch_size: 16
- eval_batch_size: 16
- seed: 1337
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: polynomial
- training_steps: 10000
### Training results
| Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Accuracy Background | Accuracy Maize | Iou Background | Iou Maize |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:-------------:|:----------------:|:-------------------:|:--------------:|:--------------:|:---------:|
| 0.0529 | 1.0 | 678 | 69.3785 | 0.4391 | 0.5 | 0.8781 | 1.0 | 0.0 | 0.8781 | 0.0 |
| 0.3755 | 2.0 | 1356 | 0.9455 | 0.4391 | 0.5 | 0.8781 | 1.0 | 0.0 | 0.8781 | 0.0 |
| 0.0603 | 3.0 | 2034 | 0.0920 | 0.8356 | 0.8602 | 0.9641 | 0.9976 | 0.7227 | 0.9607 | 0.7106 |
| 0.0341 | 4.0 | 2712 | 24.6203 | 0.4391 | 0.5 | 0.8781 | 1.0 | 0.0 | 0.8781 | 0.0 |
| 0.0332 | 5.0 | 3390 | 101.5635 | 0.4391 | 0.5 | 0.8781 | 1.0 | 0.0 | 0.8781 | 0.0 |
| 0.0331 | 6.0 | 4068 | 9.6824 | 0.4391 | 0.5 | 0.8781 | 1.0 | 0.0 | 0.8781 | 0.0 |
| 0.0302 | 7.0 | 4746 | 260.7923 | 0.4391 | 0.5 | 0.8781 | 1.0 | 0.0 | 0.8781 | 0.0 |
| 0.0305 | 8.0 | 5424 | 172.8153 | 0.4391 | 0.5 | 0.8781 | 1.0 | 0.0 | 0.8781 | 0.0 |
| 0.0313 | 9.0 | 6102 | 304.2714 | 0.4391 | 0.5 | 0.8781 | 1.0 | 0.0 | 0.8781 | 0.0 |
| 0.0301 | 10.0 | 6780 | 547.2355 | 0.4391 | 0.5 | 0.8781 | 1.0 | 0.0 | 0.8781 | 0.0 |
| 0.03 | 11.0 | 7458 | 224.2607 | 0.4391 | 0.5 | 0.8781 | 1.0 | 0.0 | 0.8781 | 0.0 |
| 0.0285 | 12.0 | 8136 | 116.3474 | 0.4391 | 0.5 | 0.8781 | 1.0 | 0.0 | 0.8781 | 0.0 |
| 0.0284 | 13.0 | 8814 | 96.8429 | 0.4391 | 0.5 | 0.8781 | 1.0 | 0.0 | 0.8781 | 0.0 |
| 0.0281 | 14.0 | 9492 | 54.2593 | 0.4391 | 0.5 | 0.8781 | 1.0 | 0.0 | 0.8781 | 0.0 |
| 0.028 | 14.75 | 10000 | 0.0756 | 0.9172 | 0.9711 | 0.9804 | 0.9834 | 0.9588 | 0.9779 | 0.8566 |
### Framework versions
- Transformers 4.21.0.dev0
- Pytorch 1.10.0+cu102
- Datasets 2.3.2
- Tokenizers 0.12.1
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